In [1]}, Kaur et al. investigate the effectiveness of numerous nature-inspired computing techniques such as genetic algorithms [2]}, ant colony optimization [3]}, particle swarm optimization [4]}, and artificial bee colony [5]} for diagnosing various critical human disorders. They conclude that nature-inspired computing techniques have high accuracy for disease detection and diagnosis. However, the survey lacks a detailed comparison with state-of-the-art schemes required to investigate the efficiency of the nature-inspired techniques for real-world problems. Another study in [6]} extensively discusses hybrid models (i.e., neural networks combined with other classical methods or meta-heuristics approaches) to outline selecting well-suited ANN model for the epidemic forecasts. Reference [6]} shows that hybrid neural networks depict enhanced performance for epidemic forecasts.
